Strengthen your machine learning with advanced feature engineering techniques
This is the code repository for Hands-On Feature Engineering with Python [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Feature engineering is the most important aspect of machine learning. You know that every day you put off learning the process, you are hurting your model’s performance. Studies repeatedly prove that feature engineering can be much more powerful than the choice of algorithms. Yet the field of feature engineering can seem overwhelming and confusing. This course offers you the single best solution. In this course, all of the recommendations have been extensively tested and proven on real-world problems. You’ll find everything included: the recommendations, the code, the data sources, and the rationale. You’ll get an over-the-shoulder, step-by-step approach for every situation, and each segment can stand alone, allowing you to jump immediately to the topics most important to you. By the end of the course, you’ll have a clear, concise path to feature engineering and will enable you to get improved results by applying feature engineering techniques on your own datasets
Section 1 - Introduction to Feature Engineering
Section 2 - Implementing Feature Extraction
Section 3 - Implementing Feature Transformation
Section 4 - Implementing Feature Selection
Section 5 - Putting All Together - Building the Application
- Master the insider tips for world-class feature engineering
- Eliminate frustration and confusion in handling all aspects of features
- Dramatically reduce the time required to move to the modeling steps of the process
- Handle missing values with speed and ease
- Systematically test for feature interaction terms build new features
- Leverage advanced “target mean encoding” to maximize performance and understanding
- Handle outliers automatically with much less effort
Anyone who wants to build faster, more accurate machine learning models will benefit. This course assumes that you have basic familiarity with Python as well as machine learning concepts. The content covers material for beginners through to experts.
This course has the following requirements:
Operating system: Windows or Linux
Browser: Mozilla or Crome
IDE : Pycharm Community version
Jupyter notebook or jupyter lab